def run(self): if self.data_generation != 'dataset': dataset_dict_output = dp.generate_random_output_data( self.data_size, self.num_batches, self.mini_batch_size, round_targets=self.round_targets) else: raise NotImplementedError( f'data_generation {self.data_generation} not implemented') self.save(dataset_dict_output)
args.num_indices_per_lookup, args.num_indices_per_lookup_fixed, m_den, ln_emb, args.data_trace_file, args.data_trace_enable_padding, ) else: sys.exit( "ERROR: --data-generation=" + args.data_generation + " is not supported" ) # target data (nbatches, lT) = dp.generate_random_output_data( args.data_size, args.num_batches, args.mini_batch_size, round_targets=args.round_targets, ) ### parse command line arguments ### m_spa = args.arch_sparse_feature_size num_fea = ln_emb.size + 1 # num sparse + num dense features m_den_out = ln_bot[ln_bot.size - 1] if args.arch_interaction_op == "dot": # approach 1: all # num_int = num_fea * num_fea + m_den_out # approach 2: unique if args.arch_interaction_itself: num_int = (num_fea * (num_fea + 1)) // 2 + m_den_out else: num_int = (num_fea * (num_fea - 1)) // 2 + m_den_out